PROJECT SUMMARY: Alzheimer?s dementia (AD) remains a major cause of morbidity and mortality worldwide. While successful therapies for the disease remain elusive, there is a significant body of evidence identifying social interaction as protective of the development of AD and as a potential therapeutic intervention that reduces cognitive decline in AD. However, quantifying and qualifying social interaction for individuals in this group has largely been dependent on user self-reported surveys that are prone to bias and difficult for the AD population to complete. Current technologies lack the ability to comprehensively and accurately measure the wide number of parameters important in measuring a complex construct such as social isolation. New innovations in biomedical engineering has enabled a new class of sensors that are wearable in nature capable of providing high-fidelity, clinically-relevant data in attractive, low user-burden form factors. We present a novel, miniaturized wearable sensor intimately connected to the skin of the suprasternal notch via hypo-allergenic adhesives that enables multimodal, continuous, real-time sensing of key parameters of social interaction for individuals with AD. Our device is already capable of measuring the most number of parameters important in social interaction including speech patterns, physical activity, eating behaviors, sleep quality, and physiological markers. We use advanced signal analytics to derive meaningful insights from the signal output of our technology. The entire sensor is completely enclosed in flexible and soft medical-grade silicone that enables comfortable daily wear. It is waterproof and can be charged wirelessly to reduce user burden. This proposal centers on the final development of a wearable device with an embedded high- frequency accelerometer, an onboard high-fidelity microphone, onboard storage, onboard power, and wireless communication capabilities that enable sensor-to-sensor linking and sensor-to-smartphone linking. In addition, more signal processing capabilities will be added to enable the collection of additional metrics relevant to social interaction and a user interface for researchers. Finally, the sensor?s performance will be tested against gold-standard clinical equipment in quiet and noisy environments using a state- of-the-art virtual sound room with individuals who have AD. Our goal is to develop a sensor useful for both researchers as a clinical outcome tool, and individuals with AD and their caretakers as an early warning system of loneliness.